哪些行为塑造了幸福?:对Twitter和Goodreads的探索性分析

Mayank Bhasin, Harshit, Pawan Goyal
{"title":"哪些行为塑造了幸福?:对Twitter和Goodreads的探索性分析","authors":"Mayank Bhasin, Harshit, Pawan Goyal","doi":"10.1145/3487351.3489475","DOIUrl":null,"url":null,"abstract":"Modeling and analysis of affective and inner states is gaining prominence in research. Articulating the entire spectrum, ranging from recipes of long-term happiness to factors leading to depression, we frame a model of happiness states of people comprising of three states: G (lasting happiness), P (flickering) and I (frustration), respectively. The definitions of these states are based on psychology literature. We used a XgBoost Classifier to categorize 54,066 Twitter users based on their tweets and analysed the results including what kind of friends each category of users have (for 120 users obtained after thresholding 213 manually labelled users). Analysing XgBoost classification we could re-confirm characteristics mentioned in the definition of the three states (G, P, I) and find out more traits/characteristics beyond the definition as well. We observed that G users are more people-oriented. G and P users are more work-oriented than I users. G users are elder in age to P or I users. I users were found to be more religious than P owing to shelter-seeking traits. Qualitative analysis shows that G group suggests long-term vision, selfless and positive qualities, religious mindset and positive demeanour as expected. I group suggests negative feelings and activities and sensual words as expected. P group has traces of both G and I. P group contains dominating, strong words and extreme negative reactions. We found 21,115 users having Twitter and Goodreads handles to study what kind of books users of each category read. Reading patterns of G constitute of academic/technical, religion, inspirational/self-help and romance. Those of P users are fantasy/fiction, sports, LGBT/BDSM/Erotica and horror/violence/betrayal. I users tend to read fantasy/fiction, death and indiscriminately any arbitrary topic. G and P users make friends in the same category whereas I users tend to have friends in P category, but not among themselves.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Which acts model happiness?: an exploratory analysis on Twitter and Goodreads\",\"authors\":\"Mayank Bhasin, Harshit, Pawan Goyal\",\"doi\":\"10.1145/3487351.3489475\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Modeling and analysis of affective and inner states is gaining prominence in research. Articulating the entire spectrum, ranging from recipes of long-term happiness to factors leading to depression, we frame a model of happiness states of people comprising of three states: G (lasting happiness), P (flickering) and I (frustration), respectively. The definitions of these states are based on psychology literature. We used a XgBoost Classifier to categorize 54,066 Twitter users based on their tweets and analysed the results including what kind of friends each category of users have (for 120 users obtained after thresholding 213 manually labelled users). Analysing XgBoost classification we could re-confirm characteristics mentioned in the definition of the three states (G, P, I) and find out more traits/characteristics beyond the definition as well. We observed that G users are more people-oriented. G and P users are more work-oriented than I users. G users are elder in age to P or I users. I users were found to be more religious than P owing to shelter-seeking traits. Qualitative analysis shows that G group suggests long-term vision, selfless and positive qualities, religious mindset and positive demeanour as expected. I group suggests negative feelings and activities and sensual words as expected. P group has traces of both G and I. P group contains dominating, strong words and extreme negative reactions. We found 21,115 users having Twitter and Goodreads handles to study what kind of books users of each category read. Reading patterns of G constitute of academic/technical, religion, inspirational/self-help and romance. Those of P users are fantasy/fiction, sports, LGBT/BDSM/Erotica and horror/violence/betrayal. I users tend to read fantasy/fiction, death and indiscriminately any arbitrary topic. G and P users make friends in the same category whereas I users tend to have friends in P category, but not among themselves.\",\"PeriodicalId\":320904,\"journal\":{\"name\":\"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3487351.3489475\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3487351.3489475","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

情感状态和内心状态的建模和分析在研究中日益突出。从长期幸福的秘诀到导致抑郁的因素,我们阐述了整个范围,构建了一个由三种状态组成的人的幸福状态模型:G(持久的幸福),P(闪烁的幸福)和I(沮丧)。这些状态的定义基于心理学文献。我们使用XgBoost Classifier根据推文对54,066名Twitter用户进行了分类,并分析了结果,包括每个类别的用户拥有什么样的朋友(对213个手动标记的用户阈值后获得的120个用户)。通过分析XgBoost分类,我们可以重新确认三种状态(G, P, I)定义中提到的特征,并发现更多超出定义的特征/特征。我们观察到G用户更以人为本。G和P用户比I用户更注重工作。G用户的年龄比P或I用户大。由于寻求庇护的特点,I用户被发现比P用户更虔诚。定性分析表明,G组如预期的那样具有长远的眼光、无私和积极的品质、宗教心态和积极的举止。第一组建议消极的情绪和活动以及预期的感官词汇。P组既有G的痕迹,也有i的痕迹。P组包含支配性的、强势的言语和极端的负面反应。我们找到了21,115名拥有Twitter和Goodreads用户名的用户,以研究每个类别的用户阅读的书籍类型。G的阅读模式包括学术/技术、宗教、励志/自助和浪漫。P用户的那些是幻想/小说,体育,LGBT/BDSM/色情和恐怖/暴力/背叛。I用户倾向于阅读幻想/小说、死亡和任何不加区分的任意主题。G用户和P用户会结交同一类别的朋友,而I用户倾向于结交P类别的朋友,但他们之间没有。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Which acts model happiness?: an exploratory analysis on Twitter and Goodreads
Modeling and analysis of affective and inner states is gaining prominence in research. Articulating the entire spectrum, ranging from recipes of long-term happiness to factors leading to depression, we frame a model of happiness states of people comprising of three states: G (lasting happiness), P (flickering) and I (frustration), respectively. The definitions of these states are based on psychology literature. We used a XgBoost Classifier to categorize 54,066 Twitter users based on their tweets and analysed the results including what kind of friends each category of users have (for 120 users obtained after thresholding 213 manually labelled users). Analysing XgBoost classification we could re-confirm characteristics mentioned in the definition of the three states (G, P, I) and find out more traits/characteristics beyond the definition as well. We observed that G users are more people-oriented. G and P users are more work-oriented than I users. G users are elder in age to P or I users. I users were found to be more religious than P owing to shelter-seeking traits. Qualitative analysis shows that G group suggests long-term vision, selfless and positive qualities, religious mindset and positive demeanour as expected. I group suggests negative feelings and activities and sensual words as expected. P group has traces of both G and I. P group contains dominating, strong words and extreme negative reactions. We found 21,115 users having Twitter and Goodreads handles to study what kind of books users of each category read. Reading patterns of G constitute of academic/technical, religion, inspirational/self-help and romance. Those of P users are fantasy/fiction, sports, LGBT/BDSM/Erotica and horror/violence/betrayal. I users tend to read fantasy/fiction, death and indiscriminately any arbitrary topic. G and P users make friends in the same category whereas I users tend to have friends in P category, but not among themselves.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Predicting COVID-19 with AI techniques: current research and future directions Predictions of drug metabolism pathways through CYP 3A4 enzyme by analysing drug-target interactions network graph An insight into network structure measures and number of driver nodes Temporal dynamics of posts and user engagement of influencers on Facebook and Instagram Vibe check: social resonance learning for enhanced recommendation
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1